Overall Corridor Analysis

Broad Descriptive Stats

# ZOE - what's going on here? something we can delete?

The Olney transit corridor runs along ordered_stop_dat$on_street %>% unique() %>% create_route_sentence() between first(ordered_stop_dat$at_street) and last(ordered_stop_dat$at_street). The corridor serves the routes create_route_sentence(unique(stop_route_analytics$route_id)). There are length(subcorridors[[3]][[1]]) total bus stops along the corridor, with the average bus trip running past round(mean(subcorridors_dat[[4]][[1]]$n_stops)) stops spanning round(mean(subcorridors_dat[[4]][[1]]$distance_traveled),digits=2) miles.

# map of routes that exist in this corridor
leaflet() %>%
  setView(lng = -75.14511, lat = 40.03905, zoom = 13) %>% 
  addProviderTiles(providers$Stamen.Toner) %>% 
  addCircleMarkers(daily_stop_analytics, lat = daily_stop_analytics$stop_lat, lng = daily_stop_analytics$stop_lon, radius = (daily_stop_analytics$total_ons + daily_stop_analytics$total_offs)/100, color = "blue")
  #addPolylines(data = routes_w_ridership, color = "#4377bc", weight = 4, layerId = link_stop_data$fromto, opacity = 0.5)

# also add chart/table of global averages for context of subcorridors

Corridor Level Descriptive Stats

These charts illustrate characteristics for the Olney corridor as a whole, such as average speed and ridership. Average speed, as well as ridership., vary both by route and by time of day.

table_1 <- full_corridor_results$route_analytics[[1]] %>% bind_rows(full_corridor_results$analytics[[1]] %>% mutate(route_id = "Total"))
 
kable(table_1, booktabs = TRUE, align = 'c',format.args = list(big.mark = ","),digits=1) %>%
  kable_styling(latex_options = "scale_down")  %>%
  row_spec(dim(table_1)[1], bold = T) %>% # format last row
  column_spec(1, italic = T) %>%  # format first column
  scroll_box(width = "100%", height = "300px")
route_id daily_ridership trips routes_served service_hours riders_per_hour on_off dwell_observed_mean dwell_predicted_mean dwell_hybrid_mean dwell_per_onoff onoff_per_trip onoff_per_tripstop avg_segment_speed avg_speed_10_pct avg_speed_25_pct avg_speed_75_pct avg_speed_90_pct
18 6,826 235 18 19.8 345.1 3,896.5 0 83.7 83.7 0.2 16.6 1.5 12.0 9.6 10.3 12.6 14.5
26 4,966 227 26 20.1 247.4 2,072.4 0 71.0 71.0 0.1 9.1 0.9 11.1 9.6 10.3 11.9 13.2
Total 11,792 462 18, 26 39.9 295.9 5,968.9 0 77.5 77.5 0.2 12.9 1.2 11.6 9.6 10.3 12.1 14.0 
# ggplot(route_analytics, aes(x = route_id, y = avg_segment_speed)) + 
#   geom_bar(stat = "identity", fill="skyblue", alpha=0.7) +
#   geom_errorbar(data = route_analytics, stat = "identity", ymin = route_analytics$avg_speed_10_pct, ymax = route_analytics$avg_speed_90_pct, colour="orange", alpha=0.6, size=1.3, width = 0.4) + 
#   scale_y_continuous(name = "Speed (MPH) - Includes Dwell Time", n.breaks = 8, limits = c(0, max(route_analytics$avg_speed_90_pct) * 1.1)) +
#   scale_x_discrete(name = "Route Number") + 
#   labs(title = paste0("The Average Bus Travels at ", round(mean(analytics$avg_segment_speed), 1), " MPH on the Corridor")) +
#   theme(text = element_text(size = 9))


#will have to change the Olney plots to work with the new function structure
plot_hourly_speed(full_corridor_results$hourly_analytics[[1]], "Olney Avenue")
plot_speed_by_period(full_corridor_results$binned_analytics[[1]], "Olney Avenue")
plot_ridership_by_period(full_corridor_results$binned_analytics[[1]], "Olney Avenue")

Corridor/Route Level Stats

Here, we separate overall corridor statistics by route to better understand the makeup of Olney bus traffic. The first three graphs, which cover ridership (both average daily ridership and average ridership per hour), the number of trips, and number of service hours per route, aggregate for both directions of each route. Next, average hourly ridership is again shown, but divided by whether the bus was Eastbound or Westbound (as Olney runs E-W). Average corridor running time, or the time it takes for a bus to make one trip along the corridor, and average hourly speed are also differentiated by direction.

# plot_daily_ridership_trips(subcorridors_dat,"OL")
# 
# plot_service_hrs(subcorridors_dat,"OL")
# 
# plot_ridership_by_route(subcorridors_dat, "OL")
# 
# plot_ridership_by_route_dir(subcorridors_dat, "OL")
# 
# plot_running_time(subcorridors_dat, "OL")
# 
# plot_speed_by_route_dir(subcorridors_dat, "OL")

Note: average speed and running time are calculated for the entire running time, including dwell times.

Sub-Corridor Analysis

There are three main “sub-corridors” within the Olney corridor: Chelten to Broad, which is the section west of Broad St, Broad to 7th, and 7th to Front, which are both east of Broad St.

Chelten to Broad

#stop_cb <- c("SEPTA372","SEPTA15911","SEPTA15794", "SEPTA15912", "SEPTA15789", "SEPTA15915", "SEPTA15586", "SEPTA15793", "SEPTA15913","SEPTA15791", "SEPTA15914", "SEPTA15792", "SEPTA15786", "SEPTA15916", "SEPTA15782", "SEPTA15917", "SEPTA15587", "SEPTA16979", "SEPTA15779", "SEPTA15918", "SEPTA15919")

subcorridors_results$daily_ridership_trips_plot[[1]]
## TableGrob (1 x 2) "arrange": 2 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
subcorridors_results$hourly_speed_plot[[1]]
subcorridors_results$speed_by_route_dir_plot[[1]]

Broad to 7th

# stop_b7 <- c("SEPTA15796", "SEPTA15908", "SEPTA15798", "SEPTA15907", "SEPTA15799", "SEPTA15800", "SEPTA16966", "SEPTA15814", "SEPTA16965", "SEPTA381", "SEPTA15795", "SEPTA373", "SEPTA15910")

subcorridors_results$daily_ridership_trips_plot[[2]]
## TableGrob (1 x 2) "arrange": 2 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
subcorridors_results$hourly_speed_plot[[2]]
subcorridors_results$speed_by_route_dir_plot[[2]]

7th to Front

#stop_7f <- c("SEPTA16963", "SEPTA15815", "SEPTA16964", "SEPTA15817","SEPTA15819", "SEPTA16961", "SEPTA32312", "SEPTA16960", "SEPTA15820", "SEPTA16959", "SEPTA15822", "SEPTA15697", "SEPTA15899", "SEPTA15818", "SEPTA16962")

subcorridors_results$daily_ridership_trips_plot[[3]]
## TableGrob (1 x 2) "arrange": 2 grobs
##   z     cells    name           grob
## 1 1 (1-1,1-1) arrange gtable[layout]
## 2 2 (1-1,2-2) arrange gtable[layout]
subcorridors_results$hourly_speed_plot[[3]]
subcorridors_results$speed_by_route_dir_plot[[3]]